Accelerating wavepacket propagation with machine learning

In this work, we discuss the use of a recently introduced machine learning (ML) technique known as Fourier neural operators (FNO) as an efficient alternative to the traditional solution of the time-dependent Schrödinger equation (TDSE). FNOs are ML models which are employed in the approximated solution of partial differential equations. For a wavepacket propagating in an anharmonic potential and for a tunneling system, we show that the FNO approach can accurately and faithfully model wavepacket propagation via the density. Additionally, we demonstrate that FNOs can be a suitable replacement for traditional TDSE solvers in cases where the results of the quantum dynamical simulation are required repeatedly such as in the case of parameter optimization problems (e.g., control). The speed-up from the FNO method allows for its combination with the Markov-chain Monte Carlo approach in applications that involve solving inverse problems such as optimal and coherent laser control of the outcome of dynamical processes.

Control of Iron(II)-Tris(2,2'-Bipyridine) Light-Induced Excited-State Trapping via External Electromagnetic Fields

Light-induced excited spin-state trapping reactions in iron pyridinic complexes allow the iron's low-to-high spin transition in a sub-picosecond timescale. Employing a recently developed model for [Fe(2,2'-bipyridine)3]2+ photochemical spin-crossover reaction in conjunction with quantum wavepacket dynamics, we explore the possibility of controlling the reaction through external electromagnetic fields, aiming at stabilizing the initial metal-to-ligand charge transfer states. We show that simple Gaussian-shaped electromagnetic fields have a minor effect on the population kinetics. However, introducing vibrationally excited initial wavepacket representations allows for maintaining the population trapped in the metal-to-ligand charge transfer states. Using optimal control theory, we propose an electromagnetic field shape that increases the lifetime of metal-to-ligand charge transfer states. These results open the route for controlling the iron photochemistry through the action of external electric fields.

Quantum optimal control of multiple weakly interacting molecular rotors in the time-dependent Hartree approximation

We perform quantum optimal control simulations, based on the Time-Dependent Hartree (TDH) approximation, for systems of three to five dipole-dipole coupled OCS rotors. A control electric field is used to steer all of the individual rotors, arranged in chains and regular polygons in a plane, toward either identical or unique objectives. The goal is to explore the utility of the TDH approximation to model the field-induced dynamics of multiple interacting rotors in the weak dipole-dipole coupling regime. A stochastic hill climbing approach is employed to seek an optimal control field that achieves the desired objectives at a specified target time. We first show that multiple rotors in chain and polygon geometries can be identically oriented in the same direction; these cases do not significantly depend on the presence of the dipole-dipole interaction. Additionally, in particular geometrical arrangements, we demonstrate that individual rotors can be uniquely manipulated toward different objectives with the same field. Specifically, it is shown that for a three rotor chain, the two end rotors can be identically oriented in a specific direction while keeping the middle rotor in its ground state, and for an equilateral triangle, two rotors can be identically oriented in a specific direction while the third rotor is oriented in the opposite direction. These multirotor unique objective cases exploit the shape of the field in coordination with dipole-dipole coupling between the rotors. Comparisons to numerically exact calculations, utilizing the TDH-determined fields, are given for all optimal control studies involving systems of three rotors.

Fitting potential energy surfaces to sum-of-products form with neural networks using exponential neurons

In this paper, the use of the neural network (NN) method with exponential neurons for directly fitting ab initio data to generate potential energy surfaces (PESs) in sum-of-product form will be discussed. The utility of the approach will be highlighted using fits of CS2, HFCO, and HONO ground state PESs based upon high-level ab initio data. Using a generic interface between the neural network PES fitting, which is performed in MATLAB, and the Heidelberg multi-configuration time-dependent Hartree (MCTDH) software package, the PESs have been tested via comparison of vibrational energies to experimental measurements. The review demonstrates the potential of the PES fitting method, combined with MCTDH, to tackle high-dimensional quantum dynamics problems.

Coupled-Trajectory Quantum-Classical Approach to Electronic Decoherence in Nonadiabatic Processes

We present a novel quantum-classical approach to nonadiabatic dynamics, deduced from the coupled electronic and nuclear equations in the framework of the exact factorization of the electron-nuclear wave function. The method is based on the quasiclassical interpretation of the nuclear wave function, whose phase is related to the classical momentum and whose density is represented in terms of classical trajectories. In this approximation, electronic decoherence is naturally induced as an effect of the coupling to the nuclei and correctly reproduces the expected quantum behavior. Moreover, the splitting of the nuclear wave packet is captured as a consequence of the correct approximation of the time-dependent potential of the theory. This new approach offers a clear improvement over Ehrenfest-like dynamics. The theoretical derivation presented in this Letter is supported by numerical results that are compared to quantum mechanical calculations.

Guiding the time-evolution of a molecule: optical control by computer

The theory and computation of optical control has been developed over the last 25 years and is now a mature field of research. Initial work provided pictures of how control using light fields in simple systems may be achieved, for example using multiple excitation pathways or pulse sequences. The development of optimal control theory then provided a general method for guiding a system to its target using a shaped laser pulse. Combined with quantum dynamics simulations this has become a widely used tool, and has been applied to a range of systems to show what can be controlled. The present challenge is to gain more insight into the mechanism of control. In addition, methods need to be extended to reach the size of system of interest to technology. In this perspective article we shall give a brief overview of present capabilities and some of the recent developments in quantum dynamics and control simulations.